Automatic Cardiac Risk Management Classification using large-context Electronic Patients Health Records
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866915851137974272 |
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| author | Vitale, Jacopo Della Morte, David Bacco, Luca Merone, Mario de Groot, Mark Haitjema, Saskia Pecchia, Leandro van Es, Bram |
| author_facet | Vitale, Jacopo Della Morte, David Bacco, Luca Merone, Mario de Groot, Mark Haitjema, Saskia Pecchia, Leandro van Es, Bram |
| contents | To overcome the limitations of manual administrative coding in geriatric Cardiovascular Risk Management, this study introduces an automated classification framework leveraging unstructured Electronic Health Records (EHRs). Using a dataset of 3,482 patients, we benchmarked three distinct modeling paradigms on longitudinal Dutch clinical narratives: classical machine learning baselines, specialized deep learning architectures optimized for large-context sequences, and general-purpose generative Large Language Models (LLMs) in a zero-shot setting. Additionally, we evaluated a late fusion strategy to integrate unstructured text with structured medication embeddings and anthropometric data. Our analysis reveals that the custom Transformer architecture outperforms both traditional methods and generative \acs{llm}s, achieving the highest F1-scores and Matthews Correlation Coefficients. These findings underscore the critical role of specialized hierarchical attention mechanisms in capturing long-range dependencies within medical texts, presenting a robust, automated alternative to manual workflows for clinical risk stratification. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_09685 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Automatic Cardiac Risk Management Classification using large-context Electronic Patients Health Records Vitale, Jacopo Della Morte, David Bacco, Luca Merone, Mario de Groot, Mark Haitjema, Saskia Pecchia, Leandro van Es, Bram Computation and Language Artificial Intelligence Information Retrieval 68T50 I.2.7 To overcome the limitations of manual administrative coding in geriatric Cardiovascular Risk Management, this study introduces an automated classification framework leveraging unstructured Electronic Health Records (EHRs). Using a dataset of 3,482 patients, we benchmarked three distinct modeling paradigms on longitudinal Dutch clinical narratives: classical machine learning baselines, specialized deep learning architectures optimized for large-context sequences, and general-purpose generative Large Language Models (LLMs) in a zero-shot setting. Additionally, we evaluated a late fusion strategy to integrate unstructured text with structured medication embeddings and anthropometric data. Our analysis reveals that the custom Transformer architecture outperforms both traditional methods and generative \acs{llm}s, achieving the highest F1-scores and Matthews Correlation Coefficients. These findings underscore the critical role of specialized hierarchical attention mechanisms in capturing long-range dependencies within medical texts, presenting a robust, automated alternative to manual workflows for clinical risk stratification. |
| title | Automatic Cardiac Risk Management Classification using large-context Electronic Patients Health Records |
| topic | Computation and Language Artificial Intelligence Information Retrieval 68T50 I.2.7 |
| url | https://arxiv.org/abs/2603.09685 |